Mitigating Distribution Shift in Graph-Based Android Malware Classification via Function Metadata and LLM Embeddings

• URL: http://arxiv.org/abs/2508.06734v1
• Date: Fri, 08 Aug 2025 22:16:57 GMT
• Title: Mitigating Distribution Shift in Graph-Based Android Malware Classification via Function Metadata and LLM Embeddings
• Abstract summary: Graph-based malware classifiers can achieve over 94% accuracy on standard Android datasets.<n>But they suffer accuracy drops of up to 45% when evaluated on previously unseen malware variants from the same family.<n>We propose a robust semantic enrichment framework that enhances function call graphs with contextual features.
• Score: 13.484494807251197
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph-based malware classifiers can achieve over 94% accuracy on standard Android datasets, yet we find they suffer accuracy drops of up to 45% when evaluated on previously unseen malware variants from the same family - a scenario where strong generalization would typically be expected. This highlights a key limitation in existing approaches: both the model architectures and their structure-only representations often fail to capture deeper semantic patterns. In this work, we propose a robust semantic enrichment framework that enhances function call graphs with contextual features, including function-level metadata and, when available, code embeddings derived from large language models. The framework is designed to operate under real-world constraints where feature availability is inconsistent, and supports flexible integration of semantic signals. To evaluate generalization under realistic domain and temporal shifts, we introduce two new benchmarks: MalNet-Tiny-Common and MalNet-Tiny-Distinct, constructed using malware family partitioning to simulate cross-family generalization and evolving threat behavior. Experiments across multiple graph neural network backbones show that our method improves classification performance by up to 8% under distribution shift and consistently enhances robustness when integrated with adaptation-based methods. These results offer a practical path toward building resilient malware detection systems in evolving threat environments.

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